Decorrelation-Based Feature Discovery: body_fat
Jose Tamez
2024-05-06
1 Effect of UPSTM-Based Decorrelation on Feature Discovery
1.0.1 Loading the libraries
library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(psych)
library(whitening)
library("vioplot")
library("pls")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)1.1 Material
Data Source https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset
“Source The data were generously supplied by Dr. A. Garth Fisher who gave permission to freely distribute the data and use for non-commercial purposes.
Roger W. Johnson Department of Mathematics & Computer Science South Dakota School of Mines & Technology 501 East St. Joseph Street Rapid City, SD 57701
email address: rwjohnso@silver.sdsmt.edu web address: http://silver.sdsmt.edu/~rwjohnso”
1.2 The Data
body_fat <- read.csv("~/GitHub/LatentBiomarkers/Data/BodyFat/BodyFat.csv", header=TRUE)
### Removing density as estimator
body_fat$Density <- NULL
#par(cex=0.5)1.2.1 Standarize the names for the reporting
studyName <- "Body_Fat"
dataframe <- body_fat
outcome <- "BodyFat"
thro <- 0.40
cexheat <- 0.75
loops <- 100
bfm <- filteredFit(BodyFat~.,body_fat,fitmethod=LASSO_MIN,filtermethod=NULL,DECOR=TRUE)
plot(body_fat$BodyFat~predict(bfm,body_fat))
bfm <- filteredFit(BodyFat~.,body_fat,fitmethod=LASSO_MIN,filtermethod=NULL)
plot(body_fat$BodyFat~predict(bfm,body_fat))
bfm <- plsr(BodyFat~.,data=body_fat)
class(bfm)[1] “mvr”
prd <- predict(bfm,newdata=body_fat)[,1,5]
plot(body_fat$BodyFat~prd)
bfm <- pcr(BodyFat~.,data=body_fat)
class(bfm)[1] “mvr”
prd <- predict(bfm,newdata=body_fat)[,1,5]
plot(body_fat$BodyFat~prd)
bfm <- cppls(BodyFat~.,data=body_fat)
prd <- predict(bfm,newdata=body_fat)[,1,5]
plot(body_fat$BodyFat~prd)
bfm <- GLMNET_RIDGE_MIN(BodyFat~.,data=body_fat)
prd <- predict(bfm,newdata=body_fat)
plot(body_fat$BodyFat~prd)
bfm <- GLMNET_ELASTICNET_MIN(BodyFat~.,data=body_fat)
prd <- predict(bfm,newdata=body_fat)
plot(body_fat$BodyFat~prd)
1.3 Generaring the report
1.3.1 Libraries
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
library(multiColl)
library(car)
library("pls")
source("C:/Users/jtame/Documents/GitHub/LatentBiomarkers/RMD/RepeatedLinearCV.R")1.3.2 Data specs
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))| rows | col |
|---|---|
| 252 | 13 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500 1.3.3 Training and testing sets
set.seed(1)
trainsamples <- sample(nrow(dataframe),3*nrow(dataframe)/4)
trainingset <- dataframe[trainsamples,]
testingset <- dataframe[-trainsamples,]
pander::pander(t(summary(trainingset)))| BodyFat | Min. : 0.00 | 1st Qu.:13.60 | Median :19.50 | Mean :19.32 | 3rd Qu.:25.30 | Max. :38.10 |
| Age | Min. :22.00 | 1st Qu.:35.00 | Median :43.00 | Mean :44.62 | 3rd Qu.:53.00 | Max. :81.00 |
| Weight | Min. :118.5 | 1st Qu.:160.2 | Median :177.0 | Mean :180.1 | 3rd Qu.:198.5 | Max. :363.1 |
| Height | Min. :29.50 | 1st Qu.:68.50 | Median :70.25 | Mean :70.23 | 3rd Qu.:72.25 | Max. :77.75 |
| Neck | Min. :31.10 | 1st Qu.:36.40 | Median :38.00 | Mean :38.05 | 3rd Qu.:39.80 | Max. :51.20 |
| Chest | Min. : 79.3 | 1st Qu.: 95.4 | Median : 99.7 | Mean :101.0 | 3rd Qu.:105.3 | Max. :136.2 |
| Abdomen | Min. : 69.40 | 1st Qu.: 86.00 | Median : 91.80 | Mean : 93.05 | 3rd Qu.: 99.70 | Max. :148.10 |
| Hip | Min. : 85.0 | 1st Qu.: 95.6 | Median : 99.5 | Mean :100.2 | 3rd Qu.:103.9 | Max. :147.7 |
| Thigh | Min. :47.20 | 1st Qu.:56.30 | Median :59.00 | Mean :59.59 | 3rd Qu.:63.10 | Max. :87.30 |
| Knee | Min. :33.00 | 1st Qu.:37.10 | Median :38.50 | Mean :38.65 | 3rd Qu.:40.10 | Max. :49.10 |
| Ankle | Min. :19.10 | 1st Qu.:22.00 | Median :22.90 | Mean :23.13 | 3rd Qu.:24.00 | Max. :33.90 |
| Biceps | Min. :24.80 | 1st Qu.:30.50 | Median :32.10 | Mean :32.42 | 3rd Qu.:34.60 | Max. :45.00 |
| Forearm | Min. :21.00 | 1st Qu.:27.30 | Median :28.80 | Mean :28.66 | 3rd Qu.:30.10 | Max. :33.70 |
| Wrist | Min. :15.80 | 1st Qu.:17.60 | Median :18.30 | Mean :18.24 | 3rd Qu.:18.80 | Max. :21.40 |
varlist <- colnames(trainingset)
varlist <- varlist[varlist != outcome]1.3.4 Correlation Matrix of the Data
The heat map of the data
par(op)
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
cormat <- cor(testingset[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
pander::pander(max(abs(cormat)))0.917
par(op)
1.3.5 Decorrelating using ILAA
ILAA bootstrapped training and testing sets
trainage_DE <- ILAA(trainingset,thr=thro,Outcome=outcome,verbose=TRUE)fast | LM | Weight Age Weight Height Neck Chest Abdomen 0.07692308 1.00000000 0.15384615 0.53846154 0.84615385 0.76923077
Included: 13 , Uni p: 0.01153846 , Base Size: 1 , Rcrit: 0.1652337
1 <R=0.843,thr=0.900>, Top: 2< 1 >Fa= 2,<|><>Tot Used: 4 , Added: 2 , Zero Std: 0 , Max Cor: 0.898
2 <R=0.810,thr=0.800>, Top: 1< 5 >Fa= 2,<|><>Tot Used: 8 , Added: 5 , Zero Std: 0 , Max Cor: 0.747
3 <R=0.568,thr=0.700>, Top: 1< 1 >Fa= 2,<|><>Tot Used: 9 , Added: 1 , Zero Std: 0 , Max Cor: 0.611
4 <R=0.517,thr=0.600>, Top: 1< 1 >Fa= 2,<|><>Tot Used: 10 , Added: 1 , Zero Std: 0 , Max Cor: 0.594
5 <R=0.487,thr=0.500>, Top: 1< 1 >Fa= 2,<|><>Tot Used: 11 , Added: 1 , Zero Std: 0 , Max Cor: 0.487
6 <R=0.447,thr=0.400>, Top: 3< 1 >Fa= 4,<|><>Tot Used: 12 , Added: 4 , Zero Std: 0 , Max Cor: 0.384
7 <R=0.384,thr=0.400>
[ 7 ], 0.384463 Decor Dimension: 12 Nused: 12 . Cor to Base: 11 , ABase: 13 , Outcome Base: 0
#trainage_DE <- ILAA(trainingset,thr=thro,Outcome=outcome,verbose=TRUE,bootstrap=30)
testage_DE <- predictDecorrelate(trainage_DE,testingset)
1.3.6 The Formulas
Generating the formulas
theLaFormulas <- getLatentCoefficients(trainage_DE)
theCharformulas <- attr(theLaFormulas,"LatentCharFormulas")
pander::pander(as.matrix(theCharformulas))| La_Age | + Age + (0.356)Weight - (1.395)Chest |
| La_Neck | - (0.069)Weight + Neck |
| La_Chest | - (0.255)Weight + Chest |
| La_Abdomen | - (0.123)Weight - (0.695)Chest + Abdomen |
| La_Hip | - (0.234)Weight + Hip |
| La_Thigh | - (0.029)Weight - (0.550)Hip + Thigh |
| La_Knee | - (0.072)Weight + Knee |
| La_Ankle | - (0.036)Weight + Ankle |
| La_Biceps | - (0.081)Weight + Biceps |
| La_Forearm | - (9.93e-03)Weight - (0.391)Biceps + Forearm |
| La_Wrist | - (0.024)Weight + Wrist |
1.3.7 Formulas Network
Displaying the features associations
par(op)
transform <- attr(trainage_DE,"UPLTM") != 0
colnames(transform) <- str_remove_all(colnames(transform),"La_")
transform <- abs(transform*cor(trainingset[,rownames(transform)])) # The weights are proportional to the observed correlation
VertexSize <- attr(trainage_DE,"fscore") # The size depends on the variable independence relevance (fscore)
names(VertexSize) <- str_remove_all(names(VertexSize),"La_")
VertexSize <- 0.5+9.5*(VertexSize-min(VertexSize))/(max(VertexSize)-min(VertexSize)) # Normalization
VertexSize <- VertexSize[colnames(transform)]
gr <- graph_from_adjacency_matrix(transform,mode = "directed",diag = FALSE,weighted=TRUE)
gr$layout <- layout_with_fr
fc <- cluster_optimal(gr)
# fc <- cluster_walktrap (gr,steps=50)
plot(fc, gr,
edge.width = 2*E(gr)$weight,
vertex.size=VertexSize,
edge.arrow.size=0.5,
edge.arrow.width=0.75,
vertex.label.color="purple",
# vertex.label.cex=0.85,
# vertex.label.dist=1.2,
vertex.label.cex=(0.70 + 0.025*VertexSize),
vertex.label.dist=(0.5 + 0.05*VertexSize),
main="Feature Association")
par(op)
varratios <- attr(trainage_DE,"VarRatio")
names(varratios) <- str_remove_all(names(varratios),"La_")
fscores <- attr(trainage_DE,"fscore")
names(fscores) <- str_remove_all(names(fscores),"La_")
clustable <- as.data.frame(cbind(Variable=fc$names,
Formula=as.character(theCharformulas[paste("La_",fc$names,sep="")]),
Cluster=fc$membership,
ResidualVariance=round(varratios[fc$names],3),
Fscore=round(fscores[fc$names],3)
)
)
rownames(clustable) <- str_replace_all(rownames(clustable),"__","_")
clustable$Variable <- NULL
clustable$Cluster <- as.integer(clustable$Cluster)
clustable$ResidualVariance <- as.numeric(clustable$ResidualVariance)
clustable$Fscore <- as.numeric(clustable$Fscore)
clustable <- clustable[order(-clustable$Fscore),]
clustable <- clustable[order(-clustable$ResidualVariance),]
clustable <- clustable[order(clustable$Cluster),]
pander::pander(as.matrix(clustable))| Formula | Cluster | ResidualVariance | Fscore | |
|---|---|---|---|---|
| Age | + Age + (0.356)Weight - (1.395)Chest | 1 | 0.831 | -1 |
| Chest | - (0.255)Weight + Chest | 1 | 0.193 | 2 |
| Abdomen | - (0.123)Weight - (0.695)Chest + Abdomen | 1 | 0.133 | -2 |
| Weight | NA | 2 | 1.000 | 9 |
| Ankle | - (0.036)Weight + Ankle | 2 | 0.648 | -1 |
| Wrist | - (0.024)Weight + Wrist | 2 | 0.442 | -1 |
| Neck | - (0.069)Weight + Neck | 2 | 0.311 | -1 |
| Knee | - (0.072)Weight + Knee | 2 | 0.259 | -1 |
| Thigh | - (0.029)Weight - (0.550)Hip + Thigh | 3 | 0.183 | -2 |
| Hip | - (0.234)Weight + Hip | 3 | 0.102 | 0 |
| Forearm | - (9.93e-03)Weight - (0.391)Biceps + Forearm | 4 | 0.511 | -2 |
| Biceps | - (0.081)Weight + Biceps | 4 | 0.347 | 0 |
1.3.8 Correlation Matrix of the Data
The heat map of the ILAA transformed data
par(op)
varlistDe <- colnames(trainage_DE)
varlistDe <- varlistDe[varlistDe != outcome]
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
# Training
cormat <- cor(trainage_DE[,varlistDe],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Training: After ILAA Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
pander::pander(max(abs(cormat)))0.384
par(op)
# Testing
cormat <- cor(testage_DE[,varlistDe],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Testing: After ILAA Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
pander::pander(max(abs(cormat)))0.54
par(op)
1.3.9 Modeling
1.3.9.1 Modeling outcome using raw set
outcomeModel <- LASSO_1SE(formula(paste(outcome,"~.")),trainingset);
predOutcome <- predict(outcomeModel,testingset)
pander::pander(as.matrix(outcomeModel$coef))| (Intercept) | -23.4024 |
| Age | 0.0321 |
| Height | -0.0931 |
| Abdomen | 0.5748 |
| Wrist | -0.3093 |
1.3.9.3 Univariate t-test
rawunittvalues <- apply(as.matrix(testingset[,names(outcomeModel$coef)[-1]]),2,tvals,testingset[,outcome])
names(rawunittvalues) <- names(outcomeModel$coef)[-1]
deunittvalues <- apply(testage_DE[,names(outcomeModel_DE$coef)[-1]],2,tvals,testingset[,outcome])1.3.9.4 Comparing summaries
psig <- 0.1/(ncol(testingset)-1)
lmod <- lm(paste(outcome,"~."),testingset[,c(outcome,names(outcomeModel$coef)[-1])])
try(vifx <-vif(lm(paste(outcome,"~."),testingset[,c(outcome,names(outcomeModel$coef)[-1])])))
sm <- summary(lmod)
if (length(lmod$coef)>10)
{
sm$coefficients[1,4] <- 1.0
gcoef <- lmod$coef[sm$coefficients[,4]<psig]
lmod <- lm(paste(outcome,"~."),testingset[,c(outcome,names(gcoef))])
try(vifx <-vif(lm(paste(outcome,"~."),testingset[,c(outcome,names(gcoef))])))
}
sm <- summary(lmod)
smcoef <- as.data.frame(sm$coefficients)
smcoef <- smcoef[order(-abs(smcoef[,3])),]
smcoef$Uni_t_values <- rawunittvalues[rownames(smcoef)]
if (!inherits(vif,"try-error")) smcoef$vif <-vifx[rownames(smcoef)]
smcoef <- smcoef[!is.na(smcoef$Uni_t_values),]
if (nrow(smcoef)>10) smcoef <- smcoef[smcoef[,4]<psig,]
pander::pander(smcoef)| Estimate | Std. Error | t value | Pr(>|t|) | Uni_t_values | vif | |
|---|---|---|---|---|---|---|
| Abdomen | 0.7784 | 0.0633 | 12.301 | 8.37e-18 | 11.12 | 1.38 |
| Height | -0.6339 | 0.2766 | -2.292 | 2.56e-02 | -1.88 | 1.70 |
| Wrist | -1.9320 | 0.8755 | -2.207 | 3.13e-02 | 1.64 | 1.87 |
| Age | 0.0385 | 0.0549 | 0.701 | 4.86e-01 | 2.23 | 1.49 |
pander::pander(t(c(R2=sm$r.squared,adj_R2=sm$adj.r.squared)))| R2 | adj_R2 |
|---|---|
| 0.768 | 0.752 |
pander::pander(c(numvar=nrow(smcoef)))| numvar |
|---|
| 4 |
lmod_DE <- lm(paste(outcome,"~."),testage_DE[,c(outcome,names(outcomeModel_DE$coef)[-1])])
try(vifx <-vif(lm(paste(outcome,"~."),testage_DE[,c(outcome,names(outcomeModel_DE$coef)[-1])])))
sm <- summary(lmod_DE)
if (length(lmod_DE$coef)>10)
{
sm$coefficients[1,4] <- 1.0
gcoef <- lmod_DE$coef[sm$coefficients[,4]<psig]
lmod_DE <- lm(paste(outcome,"~."),testage_DE[,c(outcome,names(gcoef))])
try(vifx <-vif(lm(paste(outcome,"~."),testage_DE[,c(outcome,names(gcoef))])))
}
sm <- summary(lmod_DE)
lacoef <- as.data.frame(sm$coefficients)
lacoef <- lacoef[order(-abs(lacoef[,3])),]
lacoef$Uni_t_values <- deunittvalues[rownames(lacoef)]
if (!inherits(vifx,"try-error")) lacoef$vif <-vifx[rownames(lacoef)]
lacoef <- lacoef[!is.na(lacoef$Uni_t_values),]
if (nrow(lacoef)>10) lacoef <- lacoef[lacoef[,4]<psig,]
lacoef$formula <- theCharformulas[rownames(lacoef)]
lacoef$VarRatio <- varratios[str_remove_all(rownames(lacoef),"La_")]
pander::pander(lacoef)| Estimate | Std. Error | t value | Pr(>|t|) | Uni_t_values | vif | formula | VarRatio | |
|---|---|---|---|---|---|---|---|---|
| Weight | 0.1570 | 0.0206 | 7.623 | 2.63e-10 | 4.88 | 1.00 | NA | 1.000 |
| La_Abdomen | 0.9196 | 0.1398 | 6.576 | 1.50e-08 | 5.62 | 1.10 | - (0.123)Weight - (0.695)Chest + Abdomen | 0.133 |
| La_Chest | 0.9209 | 0.1634 | 5.637 | 5.36e-07 | 5.34 | 1.22 | - (0.255)Weight + Chest | 0.193 |
| La_Thigh | -0.0345 | 0.2729 | -0.126 | 9.00e-01 | -1.79 | 1.14 | - (0.029)Weight - (0.550)Hip + Thigh | 0.183 |
pander::pander(t(c(R2=sm$r.squared,adj_R2=sm$adj.r.squared)))| R2 | adj_R2 |
|---|---|
| 0.755 | 0.738 |
pander::pander(c(numvar=nrow(lacoef)))| numvar |
|---|
| 4 |
xvals <-c(min(c(deunittvalues,rawunittvalues))-3,max(c(deunittvalues,rawunittvalues))+3)
par(mfrow=c(1,2),cex=0.5)
plot(smcoef[,c(3,5)],
main="Raw: Univariate t-values vs regression t-values",
xlim=xvals,
ylim=xvals
)
lmtvals <- lm(smcoef[,5]~smcoef[,3])
pred <- lmtvals$coefficients[1] + lmtvals$coefficients[2] * xvals
lines(x=xvals,y=pred,col="red")
text(xvals[1]+(xvals[2]-xvals[1])/2,xvals[2]-1,sprintf("Slope= %.2f",lmtvals$coefficients[2]))
plot(lacoef[-1,c(3,5)],
main="ILAA: Univariate t-values vs regression t-values",
xlim=xvals,
ylim=xvals
)
lmtvals <- lm(lacoef[,5]~lacoef[,3])
pred <- lmtvals$coefficients[1] + lmtvals$coefficients[2] * xvals
lines(x=xvals,y=pred,col="red")
text(xvals[1]+(xvals[2]-xvals[1])/2,xvals[2]-1,sprintf("Slope= %.2f",lmtvals$coefficients[2]))
#pander::pander(summary(lmtvals))
pander::pander(cor.test(smcoef[,3],smcoef[,5]))| Test statistic | df | P value | Alternative hypothesis | cor |
|---|---|---|---|---|
| 5.35 | 2 | 0.0332 * | two.sided | 0.967 |
pander::pander(cor.test(lacoef[,3],lacoef[,5]))| Test statistic | df | P value | Alternative hypothesis | cor |
|---|---|---|---|---|
| 4.6 | 2 | 0.0441 * | two.sided | 0.956 |
par(op)
1.3.9.5 Ploting predictions
par(mfrow=c(1,3),cex=0.5)
plot(lmod$fitted.values,predOutcome,main="Raw: lm train predict vs. test predict",xlab="Train",ylab="Test")
plot(lmod_DE$fitted.values,predOutcome_DE,main="ILAA: lm train predict vs. test predict",xlab="Train",ylab="Test")
plot(predOutcome,predOutcome_DE,xlab="Raw Predicted",ylab="ILAA Predicted",main="Raw vs. ILAA")
par(op)1.3.10 CV
1.3.10.1 test Correlations
par(op)
corresults <- CV_IDeA(dataframe,outcome,loops=loops)………. ………. ………. ………. ………. ………. ………. ………. ………. ……….
mintvals <- min(c(min(corresults$rawtValues),min(corresults$detValues)))
maxvals <- max(c(max(corresults$rawtValues),max(corresults$detValues)))
xvals <- c(mintvals,maxvals)
vioplot(list(raw=corresults$testRawCorrelations,ILAA=corresults$testDeCorrelations),
ylab="Pearson Correlation",
main="Test Correlations")
pander::pander(t.test(corresults$testDeCorrelations,corresults$testRawCorrelations,paired=TRUE))| Test statistic | df | P value | Alternative hypothesis | mean difference |
|---|---|---|---|---|
| 3.37 | 99 | 0.00108 * * | two.sided | 0.0048 |
sylim <- c(1,min(c(20,max(corresults$VIFRaw))))
vioplot(list(raw=corresults$VIFRaw,ILAA=corresults$VIFDe),
ylab="VIF",
ylim=sylim,
main="Test VIF")
pander::pander(summary(cbind(raw=corresults$VIFRaw,ILAA=corresults$VIFDe)))| raw | ILAA |
|---|---|
| Min. : 1.000 | Min. :1.001 |
| 1st Qu.: 1.928 | 1st Qu.:1.131 |
| Median : 2.539 | Median :1.323 |
| Mean : 4.752 | Mean :1.642 |
| 3rd Qu.: 3.707 | 3rd Qu.:1.787 |
| Max. :43.687 | Max. :5.264 |
summary(corresults$VIFRaw)Min. 1st Qu. Median Mean 3rd Qu. Max. 1.000 1.928 2.539 4.752 3.707 43.687
1.3.11 The t-values
par(op)
par(mfrow=c(1,2),cex=0.5)
plot(corresults$rawtValues,
main="Raw: Univariate t-values vs Model t-values",
xlab="Univariate",
ylab="Model",
xlim=xvals,
ylim=xvals)
lmtvals <- lm(Model~.,corresults$rawtValues)
pred <- lmtvals$coefficients[1] + lmtvals$coefficients[2] * xvals
lines(x=xvals,y=pred,col="red")
text(xvals[1]+(xvals[2]-xvals[1])/2,xvals[2]-1,sprintf("Slope= %.2f",lmtvals$coefficients[2]))
pander::pander(summary(lmtvals))| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -2.122 | 0.146 | -14.6 | 3.46e-40 |
| Uni | 0.912 | 0.025 | 36.5 | 6.21e-141 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 486 | 2.27 | 0.733 | 0.733 |
plot(corresults$detValues,
main="ILAA: Univariate t-values vs Model t-values",
xlab="Univariate",
ylab="Model",
xlim=xvals,
ylim=xvals)
lmtvals <- lm(Model~.,corresults$detValues)
pred <- lmtvals$coefficients[1] + lmtvals$coefficients[2] * xvals
lines(x=xvals,y=pred,col="red")
text(xvals[1]+(xvals[2]-xvals[1])/2,xvals[2]-1,sprintf("Slope= %.2f",lmtvals$coefficients[2]))
pander::pander(summary(lmtvals))| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 1.09 | 0.1159 | 9.4 | 2.54e-19 |
| Uni | 1.12 | 0.0295 | 38.0 | 9.00e-144 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 461 | 1.9 | 0.759 | 0.758 |
1.4 PCA, EFA, PLS, ERT
toPCA <- sapply(apply(dataframe,2,unique),length) >= 5 & colnames(dataframe) != outcome
pc <- prcomp(dataframe[,toPCA],center = TRUE,scale. = TRUE,tol=0.01) #principal components
if (ncol(dataframe)<20)
{
pander::pander(as.data.frame(pc$rotation),caption="PCA")
}| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | PC10 | PC11 | PC12 | PC13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 0.00985 | 0.75070 | -0.419893 | 0.07900 | 0.04005 | 0.2939 | -0.03363 | -0.2071 | -0.15238 | -0.26178 | 0.0306 | 0.16565 | -0.04106 |
| Weight | 0.34454 | -0.01806 | 0.039220 | 0.08660 | -0.14167 | -0.0310 | 0.07599 | 0.0465 | 0.06070 | 0.01896 | -0.2058 | 0.19013 | -0.87200 |
| Height | 0.10114 | -0.46854 | -0.677696 | 0.08215 | -0.48497 | 0.1152 | 0.13421 | -0.1019 | 0.00463 | -0.12419 | 0.0619 | -0.00815 | 0.08952 |
| Neck | 0.30559 | 0.08957 | -0.120770 | -0.20642 | -0.05487 | -0.5607 | 0.00685 | 0.1147 | -0.70288 | 0.04784 | -0.0729 | -0.01296 | 0.09503 |
| Chest | 0.31614 | 0.20916 | 0.061085 | -0.00879 | -0.15233 | -0.0702 | 0.45007 | 0.0608 | 0.24786 | 0.43054 | 0.3976 | 0.40910 | 0.21413 |
| Abdomen | 0.31180 | 0.26462 | 0.122093 | 0.11998 | -0.22918 | 0.0331 | 0.29455 | 0.0862 | 0.14014 | -0.08630 | -0.0367 | -0.79066 | 0.04559 |
| Hip | 0.32586 | 0.00309 | 0.220733 | 0.17752 | -0.16272 | 0.0451 | -0.04918 | 0.1010 | 0.13409 | -0.32280 | -0.6086 | 0.34279 | 0.40291 |
| Thigh | 0.31009 | -0.12318 | 0.321658 | 0.07652 | -0.09620 | 0.0620 | -0.27274 | -0.0409 | -0.11467 | -0.52247 | 0.6312 | 0.07313 | -0.01778 |
| Knee | 0.30830 | -0.04964 | -0.000818 | 0.24672 | -0.00528 | 0.4967 | -0.44308 | 0.1367 | -0.27248 | 0.54033 | -0.0127 | -0.08786 | 0.07582 |
| Ankle | 0.23053 | -0.22414 | -0.127712 | 0.50044 | 0.67906 | -0.0320 | 0.34680 | -0.1669 | -0.10614 | -0.08013 | 0.0112 | -0.02363 | 0.03489 |
| Biceps | 0.29934 | -0.04912 | 0.075586 | -0.32234 | 0.03479 | -0.0451 | -0.14989 | -0.8446 | 0.12914 | 0.14712 | -0.1099 | -0.08073 | 0.04901 |
| Forearm | 0.24974 | -0.13407 | -0.070634 | -0.68291 | 0.30995 | 0.4465 | 0.22753 | 0.2734 | -0.03492 | -0.15276 | -0.0428 | -0.00493 | 0.00191 |
| Wrist | 0.27913 | 0.08084 | -0.387886 | -0.05891 | 0.26499 | -0.3522 | -0.46699 | 0.2717 | 0.51325 | -0.00721 | 0.0714 | -0.07743 | 0.03044 |
rotstd <- log10(abs(100*pc$rotation)+1.0)
gplots::heatmap.2(rotstd,
trace = "none",
dendrogram="none",
breaks=c(0,0.5,1,2,3),
# scale="row",
mar = c(5,5),
col=rainbow(4),
main = "PCA Rotation",
cexRow = cexheat,
cexCol = cexheat,
Rowv=FALSE,
Colv=FALSE,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="log(|100Rot|+1)",
xlab="Output Feature", ylab="Input Feature")
efa <- try(fa(dataframe[,toPCA],ncol(pc$rotation),rotate="varimax",warnings=FALSE)) # EFA analysis
#if (!inherits(efa,"try-error"))
#{
if (ncol(dataframe)<20)
{
pander::pander(as.data.frame(efa$weights),caption="EFA")
# lds <- efa$weights
# lds2 <- matrix(as.numeric(lds),nrow=nrow(lds),ncol=ncol(lds))
# colnames(lds2) <- colnames(lds)
# rownames(lds2) <- rownames(lds)
# pander::pander(lds2,caption="EFA")
}| MR1 | MR4 | MR7 | MR2 | MR3 | MR6 | MR5 | MR8 | MR11 | MR10 | MR9 | MR12 | MR13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 0.00767 | -0.0519 | -0.0374 | 0.5374 | -0.0940 | 0.1465 | -0.0691 | 0.3735 | -0.13322 | 0.0782 | 0.2238 | 0.3874 | 9.24e-16 |
| Weight | 0.29780 | -0.3989 | 0.2135 | -0.1412 | 2.4796 | -1.2149 | -1.7169 | 0.6369 | 0.25233 | 1.0939 | 0.2345 | 0.9460 | 1.86e-14 |
| Height | 0.01918 | -0.0650 | -0.1622 | -0.0413 | 0.2954 | 0.0365 | 0.0882 | 0.0550 | -0.01552 | -0.1574 | -0.0284 | -0.1062 | -1.90e-15 |
| Neck | -0.02601 | 0.2370 | -0.2474 | 0.0597 | -0.0465 | 0.0447 | 0.1995 | -0.1173 | -0.18373 | 0.7445 | -0.0408 | -0.0268 | -1.88e-15 |
| Chest | 0.12731 | 0.2660 | -0.0390 | -0.0686 | -0.6721 | 0.0367 | 0.2428 | -0.5590 | 1.71371 | -0.2069 | 0.2119 | 0.4767 | -3.77e-15 |
| Abdomen | 0.48727 | -0.2259 | -0.2667 | 0.5718 | -0.1819 | -0.3868 | -0.3698 | 0.1409 | -0.55684 | -0.4228 | -1.0396 | -1.9006 | -1.91e-15 |
| Hip | 0.48461 | -0.3702 | -0.0894 | -0.3197 | -0.9498 | 0.2950 | 1.2873 | -1.7477 | -1.59876 | -0.7593 | 0.6947 | 1.1673 | -8.54e-15 |
| Thigh | 0.12102 | 0.0505 | -0.0853 | -0.5021 | -0.6365 | 0.3236 | 0.4616 | 2.0758 | 0.59891 | 0.2457 | -0.1960 | -0.2491 | 4.02e-16 |
| Knee | -0.15404 | -0.0329 | 0.3065 | 0.2623 | 0.1378 | 1.4142 | -0.3833 | -0.3405 | 0.17064 | -0.0954 | -0.1017 | -0.2219 | -1.44e-15 |
| Ankle | -0.08435 | -0.0656 | 0.5510 | -0.0511 | -0.1519 | -0.1641 | -0.1428 | -0.0308 | 0.00159 | -0.0111 | -0.0337 | -0.1002 | -7.79e-16 |
| Biceps | -0.11642 | 0.5379 | -0.1024 | 0.0695 | -0.1208 | -0.0719 | -0.3077 | 0.0332 | -0.24585 | -0.2367 | 0.5555 | -0.0625 | -1.06e-15 |
| Forearm | -0.12353 | 0.5231 | -0.1039 | -0.0924 | -0.0942 | 0.0797 | -0.0999 | -0.2008 | -0.15371 | -0.1667 | -0.2532 | -0.0227 | -1.67e-16 |
| Wrist | -0.26733 | 0.1966 | 0.4301 | 0.2822 | 0.1018 | -0.1746 | 1.0769 | 0.1301 | 0.00231 | -0.2285 | -0.1205 | -0.1936 | -7.43e-16 |
rotstd <- log10(abs(100*efa$weights)+1.0)
gplots::heatmap.2(rotstd,
trace = "none",
dendrogram="none",
breaks=c(0,0.5,1,2,3),
# scale="row",
mar = c(5,5),
col=rainbow(4),
main = "EFA weights",
cexRow = cexheat,
cexCol = cexheat,
Rowv=FALSE,
Colv=FALSE,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="log(|100W|+1)",
xlab="Output Feature", ylab="Input Feature")
#}
plm <- plsr(formula=formula(paste(outcome,"~.")),data=dataframe,scale =TRUE)
if (ncol(dataframe)<20)
{
lds <- plm$loadings
lds2 <- matrix(as.numeric(lds),nrow=nrow(lds),ncol=ncol(lds))
colnames(lds2) <- colnames(lds)
rownames(lds2) <- rownames(lds)
pander::pander(lds2,caption="PLS")
}| Comp 1 | Comp 2 | Comp 3 | Comp 4 | Comp 5 | Comp 6 | Comp 7 | Comp 8 | Comp 9 | Comp 10 | Comp 11 | Comp 12 | Comp 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 0.0384 | 0.57608 | -0.6875 | 0.7478 | -0.41789 | 0.00471 | -0.0761 | 0.10245 | -0.37453 | 0.01428 | 0.1080 | 0.2376 | -0.28062 |
| Weight | 0.3562 | -0.08577 | 0.1436 | -0.0683 | -0.20422 | -0.00403 | -0.0936 | -0.04584 | 0.02295 | -0.20848 | -0.2603 | -0.2984 | -0.06567 |
| Height | 0.0760 | -0.62143 | 0.2716 | 0.5959 | -0.92161 | 0.40989 | 0.0618 | -0.14581 | -0.00432 | 0.00165 | 0.0509 | 0.1227 | -0.07420 |
| Neck | 0.3161 | -0.08098 | -0.3655 | -0.0146 | -0.13866 | 0.14203 | 0.2369 | -0.12786 | 0.53456 | -0.87736 | 0.6928 | -0.1367 | -0.13499 |
| Chest | 0.3381 | 0.15815 | 0.1224 | 0.1010 | -0.17174 | -0.21335 | -0.0436 | -0.09300 | 0.44268 | -0.14630 | -0.2084 | 0.1896 | 0.47454 |
| Abdomen | 0.3379 | 0.25099 | 0.2404 | 0.1292 | 0.01884 | 0.33627 | -0.0425 | 0.00247 | 0.16578 | 0.04487 | -0.0127 | -0.2395 | 0.06280 |
| Hip | 0.3408 | 0.00303 | 0.2523 | -0.2796 | -0.15177 | 0.00364 | -0.1979 | -0.05027 | -0.12750 | 0.58828 | 0.2592 | -0.3429 | -0.12910 |
| Thigh | 0.3195 | -0.09734 | 0.2962 | -0.4286 | 0.17340 | 0.18765 | -0.0593 | -0.14376 | -0.52426 | -0.26922 | 0.2559 | 0.3428 | 0.00953 |
| Knee | 0.3161 | -0.13333 | 0.0318 | -0.1155 | -0.23851 | 0.06294 | 0.1724 | 0.70035 | -0.51326 | 0.22642 | -0.1638 | 0.0435 | 0.09049 |
| Ankle | 0.2245 | -0.35117 | -0.0426 | 0.1850 | 0.47509 | -0.27208 | -0.8964 | 0.62422 | 0.03005 | -0.20293 | 0.1301 | 0.1241 | -0.15447 |
| Biceps | 0.3067 | -0.13624 | -0.0459 | -0.0987 | 0.23737 | -0.12850 | 0.2999 | -0.27227 | 0.23005 | 0.33561 | -0.5523 | 0.8472 | -0.66102 |
| Forearm | 0.2489 | -0.25932 | -0.0386 | 0.2871 | 0.59972 | -0.71664 | 0.6784 | -0.30636 | -0.21534 | 0.04722 | 0.2028 | -0.3232 | 0.16145 |
| Wrist | 0.2828 | -0.20726 | -0.7423 | 0.2230 | -0.00939 | 0.28827 | -0.1485 | -0.15622 | -0.06604 | 0.46004 | -0.3119 | -0.2325 | 0.39020 |
loadadings <- log10(abs(100*plm$loadings) + 1.0)
gplots::heatmap.2(loadadings,
breaks=c(0,0.5,1,2,3),
trace = "none",
dendrogram="none",
# scale="row",
mar = c(5,5),
col=rainbow(4),
main = "PLS Loadings",
cexRow = cexheat,
cexCol = cexheat,
Rowv=FALSE,
Colv=FALSE,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="log(|100Beta|+1)",
xlab="Output Feature", ylab="Input Feature")
ERTmod <- ILAA(dataframe,Outcome = outcome,thr=thro)
ERT <- log10(abs(100*attr(ERTmod,"UPLTM")) + 1);
gplots::heatmap.2(ERT,
trace = "none",
breaks=c(0,0.5,1,2,3),
mar = c(5,5),
col=rainbow(4),
main = "ERT Rotation",
cexRow = cexheat,
cexCol = cexheat,
dendrogram="none",
Rowv=FALSE,
Colv=FALSE,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="log(|100Beta|+1)",
xlab="Output Feature", ylab="Input Feature")
if (ncol(dataframe)<20)
{
pander::pander(attr(ERTmod,"UPLTM"),caption="ERT")
}| La_Age | Weight | La_Neck | La_Chest | La_Abdomen | La_Hip | La_Thigh | La_Knee | La_Ankle | La_Biceps | La_Forearm | La_Wrist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 1.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Weight | 0.359 | 1 | -0.0687 | -0.257 | 0.00 | -0.229 | -0.0394 | -0.07 | -0.0354 | -0.0823 | -0.0433 | -0.0232 |
| Neck | 0.000 | 0 | 1.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Chest | -1.401 | 0 | 0.0000 | 1.000 | -1.17 | 0.000 | 0.0000 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Abdomen | 0.000 | 0 | 0.0000 | 0.000 | 1.00 | 0.000 | 0.0000 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Hip | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 1.000 | -0.5050 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Thigh | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 1.0000 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Knee | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 1.00 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Ankle | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 0.00 | 1.0000 | 0.0000 | 0.0000 | 0.0000 |
| Biceps | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 0.00 | 0.0000 | 1.0000 | 0.0000 | 0.0000 |
| Forearm | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 0.00 | 0.0000 | 0.0000 | 1.0000 | 0.0000 |
| Wrist | 0.000 | 0 | 0.0000 | 0.000 | 0.00 | 0.000 | 0.0000 | 0.00 | 0.0000 | 0.0000 | 0.0000 | 1.0000 |
1.5 U-MAP Visualization of features
1.5.1 The UMAP on Raw Data
thesamples <- c(1:nrow(dataframe));
if (nrow(dataframe)>2000)
{
thesamples <- sample(thesamples,2000)
}
classes <- as.integer(scale(dataframe[thesamples,outcome]))
classes <- classes - min(classes) + 1
raincolors <- heat.colors(length(unique(classes)))
dtatoplot <- as.matrix(FRESAScale(dataframe[thesamples,],method="OrderLogit")$scaledData)
datasetframe.umap = umap(dtatoplot,n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: RAW",col=raincolors[classes],pch=15)
gplots::heatmap.2(dtatoplot,
trace = "none",
mar = c(5,5),
col=heat.colors(5),
main = "Raw",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Z",
xlab="Feature", ylab="Subject")
dtatoplot <- as.matrix(FRESAScale(predict(pc,dataframe[thesamples,]),method="OrderLogit")$scaledData)
datasetframe.umap = umap(dtatoplot,n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: PCA",col=raincolors[classes],pch=15)
gplots::heatmap.2(dtatoplot,
trace = "none",
mar = c(5,5),
col=heat.colors(5),
main = "PCA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Z",
xlab="Feature", ylab="Subject")
if (!inherits(efa,"try-error"))
{
dtatoplot <- as.matrix(FRESAScale(predict(efa,dataframe[thesamples,toPCA]),method="OrderLogit")$scaledData)
datasetframe.umap = umap(dtatoplot,n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: EFA",col=raincolors[classes],pch=15)
gplots::heatmap.2(dtatoplot,
trace = "none",
mar = c(5,5),
col=heat.colors(5),
main = "EFA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Z",
xlab="Feature", ylab="Subject")
}
rotframe <- as.matrix(scale(dataframe[thesamples,rownames(plm$loadings)])) %*% plm$loadings
dtatoplot <- as.matrix(FRESAScale(rotframe,method="OrderLogit")$scaledData)
datasetframe.umap = umap(dtatoplot,n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: PLS",col=raincolors[classes],pch=15)
gplots::heatmap.2(dtatoplot,
trace = "none",
mar = c(5,5),
col=heat.colors(5),
main = "PLS",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Z",
xlab="Feature", ylab="Subject")
dtatoplot <- as.matrix(FRESAScale(ERTmod[thesamples,colnames(ERTmod) != outcome],method="OrderLogit")$scaledData)
datasetframe.umap = umap(dtatoplot,n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: ERT",col=raincolors[classes],pch=15)
gplots::heatmap.2(dtatoplot,
trace = "none",
mar = c(5,5),
col=heat.colors(5),
main = "ERT",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Z",
xlab="Feature", ylab="Subject")
1.6 The Body Fat plots
Final plots
plot(predOutcome,testingset[,outcome],xlab="Raw Predicted",ylab=outcome,main="Raw Fat Prediction")
plot(predOutcome_DE,testingset[,outcome],xlab="IDeA Predicted",ylab=outcome,main="IDeA Fat Prediction")